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Learning Input Strictly Local Functions: Comparing Approaches with Catalan Adjectives
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In: Proceedings of the Society for Computation in Linguistics (2022)
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Learning the surface structure of wh-questions in English and French with a non-parametric Bayesian model
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In: Proceedings of the Society for Computation in Linguistics (2021)
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Deep neural networks easily learn unnatural infixation and reduplication patterns
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In: Proceedings of the Society for Computation in Linguistics (2021)
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Extending adaptor grammars to learn phonological alternations ...
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Acoustic-phonetic and auditory mechanisms of adaptation in the perception of sibilant fricatives
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| 6 |
Computer code comprehension shares neural resources with formal logical inference in the fronto-parietal network
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In: eLife (2020)
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Extending adaptor grammars to learn phonological alternations
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In: Proceedings of the Society for Computation in Linguistics (2020)
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Abstract:
Recent advances in unsupervised learning of linguistic structure have demonstrated the feasibility of inferring latent morphological parses from an unannotated corpus given transparent underlying-to-surface mappings (ex., Adaptor Grammars), as well as in learning predictable phonological transformations from prespecified underlying morphemes to a range of surface allomorphs via a stochastic edit distance algorithm. In this paper we introduce a nonparametric Bayesian model which builds on the morpheme-segmentation success of AGs, and incorporates the ability to learn predictable phonological transformations of underlying forms to their surface allomorphs via the interaction of markedness and faithfulness principles, inspired by generative phonology. The unsupervised nature of this model (that is, no semantic information about the words being segmented is provided) is relevant not only computationally but also psychologically, as it mirrors developmental findings that young infants segment and cluster morphemes based solely on phonetic and distributional similarity. The model also incorporates many of the other cognitive restrictions infants during the initial period of morphophonological learning in an effort to make the model maximally realistic, and thus eventually useful in making quantitative predictions about the early stages of morphophonological acquisition that can be experimentally investigated. We evaluate the model on a novel dataset consisting of a complex system of allomorphy in Acehnese, an understudied Indonesian language.
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Keyword:
Acehnese; adaptor grammars; allomorphy; bayesian nonparametrics; Computational Linguistics; morphological segmentation; morphophonology; phonological acquisition
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URL: https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1165&context=scil
https://scholarworks.umass.edu/scil/vol3/iss1/57
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Re(current) reduplication: Interpretable neural network models of morphological copying
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In: Proceedings of the Society for Computation in Linguistics (2019)
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Spatial Language and the Embedded Listener Model in Parents’ Input to Children
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Integrated bilingual grammatical architecture: Insights from syntactic development
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| 15 |
Bayesian Speech Production: Evidence from Latency and Hyperarticulation
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In: Kirov, Christo; & Wilson, Colin. (2013). Bayesian Speech Production: Evidence from Latency and Hyperarticulation. Proceedings of the Cognitive Science Society, 35(35). Retrieved from: http://www.escholarship.org/uc/item/5296p4d1 (2013)
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| 16 |
Artificial grammar learning of shape-based noun classification
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In: Culbertson, Jennifer; & Wilson, Colin. (2013). Artificial grammar learning of shape-based noun classification. Proceedings of the Cognitive Science Society, 35(35). Retrieved from: http://www.escholarship.org/uc/item/38q883mm (2013)
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Acquiring a balance: Verbs in spatial language development ...
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The Specificity of Online Variation in Speech Production
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In: Kirov, Christo; & Wilson, Colin. (2012). The Specificity of Online Variation in Speech Production. Proceedings of the Cognitive Science Society, 34(34). Retrieved from: http://www.escholarship.org/uc/item/9mz1d1tx (2012)
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